%0 Journal Article
%T 双自适应衰减卡尔曼滤波锂电池荷电状态估计<br>An Estimation Method for State of Charge of Lithium？？ion Batteries Using Dual Adaptive Fading Extended Kalman Filter
%A 赵云飞
%A 徐俊
%A 王霄
%A 徐浩
%A 梅雪松
%J 西安交通大学学报
%D 2018
%R 10.7652/xjtuxb201812015
%X 针对卡尔曼滤波法在锂离子电池荷电状态(SOC)估计时存在误差较大、收敛较慢等问题,提出了一种双自适应衰减扩展卡尔曼滤波荷电状态估计(DAFEKF)算法。该算法首先设计了针对动力电池的荷电状态估计观测器,利用测得的电流和电压值分别作为观测器的输入和观测值,结合双自适应衰减扩展卡尔曼滤波估计出观测器中的电池荷电状态,在卡尔曼滤波算法的基础上加入时变衰减因子来减弱过去数据对当前滤波值的影响,并自适应地调整卡尔曼算法中过程噪声和测量噪声协方差。利用DAFEKF算法估计出的SOC结果与扩展卡尔曼滤波(EKF)和自适应扩展卡尔曼滤波(AEKF)算法进行了比较,结果表明,DAFEKF方法具有较好的准确性、鲁棒性和收敛性,使SOC估计误差控制在2％以内。<br>A dual adaptive fading extended Kalman filter(DAFEKF) algorithm is proposed for the problem of low accuracy and convergent speed of state？？of？？charge (SOC) estimation. The algorithm designs an observer of state？？of？？charge for the power battery, and the measured current and voltage are taken as input and observation values of the observer, respectively. Then the state of charge of a battery is estimated by the DAFEKF. The DAFEKF bases on the Kalman algorithm, and adds the time？？varying fading factor to reduce the influence of past data on current filtering values and to adaptively adjust the covariances of the process noise and measurement noise. SOC results of a lithium battery obtained using the proposed DAFEKF are compared with those obtained using the extended Kalman filter (EKF) and the adaptive extended Kalman filter (AEKF), and the comparison shows that the DAFEKF method provides better accuracy, robustness and convergence, and the SOC error of the proposed method is less than 2％
%K 锂离子电池
%K 荷电状态
%K 自适应卡尔曼滤波
%K 扩展卡尔曼滤波
%K 双自适应&lt
%K br&gt
%K lithium？？ion batteries
%K state of charge
%K adaptive Kalman filter
%K extended Kalman filter
%K dual adaptive
%U http://zkxb.xjtu.edu.cn/oa/DArticle.aspx?type=view&id=201812015